Pattern recognition utilizing a nanotechnology-based neural network

ABSTRACT

A pattern recognition system, comprising a neural network formed utilizing nanotechnology and a pattern input unit, which communicates with the neural network, wherein the neural network processes data input via the pattern input unit in order to recognize data patterns thereof. Such a pattern recognition system can be implemented in the context of a speech recognition system and/or other pattern recognition systems, such as visual and/or imaging recognition systems.

CROSS REFERENCE TO RELATED PATENT APPLICATION

This patent application is a continuation of U.S. patent application Ser. No. 10/095,273 entitled “Physical Neural Network Design Incorporating Nanotechnology,” which was filed on Mar. 12, 2002, now U.S. Pat. No. 6,889,216, the disclosure of which is incorporated herein by reference in its entirety.

TECHNICAL FIELD

Embodiments generally relate to nanotechnology. Embodiments also relate to neural networks and neural computing systems and methods thereof. Embodiments also relate to pattern recognition devices, methods and systems, including devices that recognize speech, visual and/or imaging data.

BACKGROUND

Neural networks are computational systems that permit computers to essentially function in a manner analogous to that of the human brain. Neural networks do not utilize the traditional digital model of manipulating 0's and 1's. Instead, neural networks create connections between processing elements, which are equivalent to neurons of a human brain. Neural networks are thus based on various electronic circuits that are modeled on human nerve cells (i.e., neurons). A neural network is an information-processing network, which is inspired by the manner in which a human brain performs a particular task or function of interest.

In general, artificial neural networks are systems composed of many nonlinear computational elements operating in parallel and arranged in patterns reminiscent of biological neural nets. The computational elements, or nodes, are connected via variable weights that are typically adapted during use to improve performance. Thus, in solving a problem, neural net models can explore many competing hypothesis simultaneously using massively parallel nets composed of many computational elements connected by links with variable weights.

In a neural network, “neuron-like” nodes can output a signal based on the sum of their inputs, the output being the result of an activation function. In a neural network, there exists a plurality of connections, which are electrically coupled among a plurality of neurons. The connections serve as communication bridges among of a plurality of neurons coupled thereto. A network of such neuron-like nodes has the ability to process information in a variety of useful ways. By adjusting the connection values between neurons in a network, one can match certain inputs with desired outputs.

One does not “program” a neural network. Instead, one “teaches” a neural network by examples. Of course, there are many variations. For instance, some networks do not require examples and extract information directly from the input data. The two variations are thus called supervised and unsupervised learning. Neural networks are currently used in applications such as noise filtering, face and voice recognition and pattern recognition. Neural networks can thus be utilized as an advanced mathematical technique for processing information.

Neural networks that have been developed to date are largely software-based. The implementation of neural network systems has lagged somewhat behind their theoretical potential due to the difficulties in building neural network hardware. This is primarily because of the large numbers of neurons and weighted connections required. The emulation of even of the simplest biological nervous systems would require neurons and connections numbering in the millions. Due to the difficulties in building such highly interconnected processors, the currently available neural network hardware systems have not approached this level of complexity. Another disadvantage of hardware systems is that they typically are often custom designed and built to implement one particular neural network architecture and are not easily, if at all, reconfigurable to implement different architectures. A true physical neural network (i.e., artificial neural network) chip, for example, has not yet been designed and successfully implemented.

The problem with a pure hardware implementation of a neural network with technology as it exists today, is the inability to physically form a great number of connections and neurons. On-chip learning can exist, but the size of the network would be limited by digital processing methods and associated electronic circuitry. One of the difficulties in creating true physical neural networks lies in the highly complex manner in which a physical neural network must be designed and built. It is believed that solutions to creating a true physical and artificial neural network lie in the use of nanotechnology and the implementation of analog variable connections.

The term “nanotechnology” generally refers to nanometer-scale manufacturing processes, materials and devices, as associated with, for example, nanometer-scale lithography and nanometer-scale information storage and include devices such as nanotubes, nanowires, nanoparticles and so forth. Nanometer-scale components find utility in a wide variety of fields, particularly in the fabrication of microelectrical and microelectromechanical systems (commonly referred to as “MEMS”). Microelectrical nano-sized components include transistors, resistors, capacitors and other nano-integrated circuit components. MEMS devices include, for example, micro-sensors, micro-actuators, micro-instruments, micro-optics, and the like.

Based on the foregoing, it is believed that a physical neural network which incorporates nanotechnology is a solution to the problems encountered by prior art neural network solutions. It is believed that a true physical neural network can be designed and constructed without relying on computer simulations for training, or relying on standard digital (binary) memory to store connections strengths. Additionally, such a physical neural network, if implemented properly, can be utilized for pattern recognition purposes, including speech, visual and/or imaging data.

BRIEF SUMMARY

The following summary is provided to facilitate an understanding of some of the innovative features unique to the embodiments, and is not intended to be a full description. A full appreciation of the various aspects of the embodiments can be gained by taking the entire specification, claims, drawings, and abstract as a whole.

It is therefore another aspect of the present to provide a physical neural network, which can be formed and implemented utilizing nanotechnology.

It is another aspect of the present invention to provide for a pattern recognition system that recognizes speech, visual data and/or imaging data and incorporates a nanotechnology-based physical neural network.

It is still another aspect of the present invention to provide a physical neural network, which can be formed from a plurality of interconnected nanoconnections or nanoconnectors.

It is yet a further aspect of the present invention to provide a physical neural network, which can be formed from a plurality of nanoconductors, such as, for example, nanowires and/or nanotubes.

It is still an additional aspect of the present invention to provide a physical neural network, which can be implemented physically in the form of a chip structure.

The above and other aspects can be achieved as is now described. A physical neural network based on nanotechnology is disclosed herein, including methods thereof. Such a physical neural network generally includes one or more neuron-like nodes, connected to a plurality of interconnected nanoconnections. Each neuron-like node sums one or more input signals and generates one or more output signals based on a threshold associated with the input signal. The physical neural network also includes a connection network formed from the interconnected nanoconnections, such that the interconnected nanoconnections used thereof by one or more of the neuron-like nodes can be strengthened or weakened according to an application of an electric field. Alignment has also been observed with a magnetic field, but electric fields are generally more practical. Note that the connection network is generally associated with one or more of the neuron-like nodes.

The output signal is generally based on a threshold below which the output signal is not generated and above which the output signal is generated. The transition from zero output to high output need not necessarily be abrupt or non linear. The connection network comprises a number of layers of nanoconnections, wherein the number of layers is generally equal to a number of desired outputs from the connection network. The nanoconnections are formed without influence from disturbances resulting from other nanoconnections thereof. Such nanoconnections may be formed from an electrically conducting material. The electrically conducting material can be selected such that a dipole is induced in the electrically conducting material in the presence of an electric field. Such a nanoconnection may comprise a nanoconductor.

The connection network itself may comprise a connection network structure having a connection gap formed therein, and a solution located within the connection gap, such that the solution comprises a solvent or suspension and one or more nanoconductors. Preferably, a plurality of nanoconductors is present in the solution (i.e., mixture). Note that such a solution may comprise a liquid and/or gas. An electric field can then be applied across the connection gap to permit the alignment of one or more of the nanoconductors within the connection gap. The nanoconductors can be suspended in the solvent, or can lie at the bottom of the connection gap on the surface of the chip. Studies have shown that nanotubes can align both in the suspension and/or on the surface of the gap. The electrical conductance of the mixture is less than the electrical conductance of the nanoconductors within the solution.

The nanoconductors within the connection gap thus experience an increased alignment in accordance with an increase in the electric field applied across the connection gap. Thus, nanoconnections of the neuron-like node that are utilized most frequently by the neuron-like node become stronger with each use thereof. The nanoconnections that are utilized least frequently become increasingly weak and eventually dissolve back into the solution. The nanoconnections may or may not comprise a resistance, which can be raised or lowered by a selective activation of a nanoconnection. They can be configured as nanoconductors such as, for example, a nanotube or nanowire. An example of a nanotube, which may be implemented in accordance with the invention described herein, is a carbon nanotube, nanowire and/or other nanoparticle. Additionally, such nanoconnections may be configured as a negative connection associated with the neuron-like node.

In general, a pattern recognition system is disclosed herein, comprising a physical neural network formed utilizing nanotechnology and a pattern input unit, which communicates with the physical neural network, wherein the physical neural network processes data input via the pattern input unit in order to recognize data patterns thereof. Such a pattern recognition system can be implemented in the context of a speech recognition system and/or other pattern recognition systems, such as visual and/or imaging recognition systems.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a graph illustrating a typical activation function that can be implemented in accordance with one embodiment;

FIG. 2 illustrates a schematic diagram illustrating a diode configuration as a neuron, in accordance with a preferred embodiment;

FIG. 3 illustrates a block diagram illustrating a network of nanowires between two electrodes, in accordance with a preferred embodiment;

FIG. 4 illustrates a block diagram illustrating a plurality of connections between inputs and outputs of a physical neural network, in accordance with a preferred embodiment;

FIG. 5 illustrates a schematic diagram of a physical neural network that can be created without disturbances, in accordance with a preferred embodiment;

FIG. 6 illustrates a schematic diagram illustrating an example of a physical neural network that can be implemented in accordance with an alternative embodiment;

FIG. 7 illustrates a schematic diagram illustrating an example of a physical neural network that can be implemented in accordance with an alternative embodiment;

FIG. 8 illustrates a schematic diagram of a chip layout for a connection network that may be implemented in accordance with an alternative embodiment;

FIG. 9 illustrates a flow chart of operations illustrating operational steps that may be followed to construct a connection network, in accordance with a preferred embodiment;

FIG. 10 illustrates a flow chart of operations illustrating operational steps that may be utilized to strengthen nanoconductors within a connection gap, in accordance with a preferred embodiment;

FIG. 11 illustrates a schematic diagram of a circuit illustrating temporal summation within a neuron, in accordance with a preferred embodiment; and

FIG. 12 illustrates a block diagram illustrating a pattern recognition system, which may be implemented with a physical neural network device, in accordance with a preferred embodiment.

DETAILED DESCRIPTION

The particular values and configurations discussed in these non-limiting examples can be varied and are cited merely to illustrate one or more embodiments.

The physical neural network described and disclosed herein is different from prior art forms of neural networks in that the disclosed physical neural network does not require a computer simulation for training, nor is its architecture based on any current neural network hardware device. The design of the physical neural network described herein with respect to particular embodiments is actually quite “organic”. Such a physical neural network is generally fast and adaptable, no matter how large such a physical neural network becomes. The physical neural network described herein can be referred to generically as a Knowm. The terms “physical neural network” and “Knowm” can be utilized interchangeably to refer to the same device, network, or structure.

Network orders of magnitude larger than current VSLI neural networks can be built and trained with a standard computer. One consideration for a Knowm is that it must be large enough for its inherent parallelism to shine through. Because the connection strengths of such a physical neural network are dependant on the physical movement of nanoconnections thereof, the rate at which a small network can learn is generally very small and a comparable network simulation on a standard computer can be very fast. On the other hand, as the size of the network increases, the time to train the device does not change. Thus, even if the network takes a full second to change a connection value a small amount, if it does the same to a billion connections simultaneously, then its parallel nature begins to express itself.

A physical neural network (i.e., a Knowm) must have two components to function properly. First, the physical neural network must have one or more neuron-like nodes that sum a signal and output a signal based on the amount of input signal received. Such a neuron-like node is generally non-linear in its output. In other words, there should be a certain threshold for input signals, below which nothing is output and above which a constant or nearly constant output is generated or allowed to pass. This is a very basic requirement of standard software-based neural networks, and can be accomplished by an activation function. The second requirement of a physical neural network is the inclusion of a connection network composed of a plurality of interconnected connections (i.e., nanoconnections). Such a connection network is described in greater detail herein.

FIG. 1 illustrates a graph 100 illustrating a typical activation function that can be implemented in accordance with one embodiment. Note that the activation function need not be non-linear, although non-linearity is generally desired for learning complicated input-output relationships. The activation function depicted in FIG. 1 comprises a linear function, and is shown as such for general edification and illustrative purposes only. As explained previously, an activation function may also be non-linear.

As illustrated in FIG. 1, graph 100 includes a horizontal axis 104 representing a sum of inputs, and a vertical axis 102 representing output values. A graphical line 106 indicates threshold values along a range of inputs from approximately −10 to +10 and a range of output values from approximately 0 to 1. As more neural networks (i.e., active inputs) are established, the overall output as indicated at line 105 climbs until the saturation level indicated by line 106 is attained. If a connection is not utilized, then the level of output (i.e., connection strength) begins to fade until it is revived. This phenomenon is analogous to short term memory loss of a human brain. Note that graph 100 is presented for generally illustrative and edification purposes only and is not considered a limiting feature of the embodiments.

In a Knowm network, the neuron-like node can be configured as a standard diode-based circuit, the diode being the most basic semiconductor electrical component, and the signal it sums may be a voltage. An example of such an arrangement of circuitry is illustrated in FIG. 2, which generally illustrates a schematic diagram illustrating a diode-based configuration as a neuron 200, in accordance with a preferred embodiment. Those skilled in the art can appreciate that the use of such a diode-based configuration is not considered a limitation of the embodiments, but merely represents one potential arrangement in which the embodiments may be implemented.

Although a diode may not necessarily be utilized, its current versus voltage characteristics are non-linear when used with associated resistors and similar to the relationship depicted in FIG. 1. The use of a diode as a neuron is thus not a limiting feature, but is only referenced herein with respect to a preferred embodiment. The use of a diode and associated resistors with respect to a preferred embodiment simply represents one potential “neuron” implementation. Such a configuration can be said to comprise an artificial neuron. It is anticipated that other devices and components may be utilized instead of a diode to construct a physical neural network and a neuron-like node (i.e., artificial neuron), as indicated here.

Thus, neuron 200 comprises a neuron-like node that may include a diode 206, which is labeled D₁, and a resistor 204, which is labeled R₂. Resistor 204 is connected to a ground 210 and an input 205 of diode 206. Additionally, a resistor 202, which is represented as a block and labeled R₁ can be connected to input 205 of diode 206. Block 202 includes an input 212, which comprises an input to neuron 200. A resistor 208, which is labeled R₃, is also connected to an output 214 of diode 206. Additionally, resistor 208 is coupled to ground 210. Diode 206 in a physical neural network is analogous to a neuron of a human brain, while an associated connection formed thereof, as explained in greater detail herein, is analogous to a synapse of a human brain.

As depicted in FIG. 2, the output 214 is determined by the connection strength of R₁ (i.e., resistor 202). If the strength of R₁'s connection increases (i.e., the resistance decreases), then the output voltage at output 214 also increases. Because diode 206 conducts essentially no current until its threshold voltage (e.g., approximately 0.6V for silicon) is attained, the output voltage will remain at zero until R₁ conducts enough current to raise the pre-diode voltage to approximately 0.6V. After 0.6V has been achieved, the output voltage at output 214 will increase linearly. Simply adding extra diodes in series or utilizing different diode types may increase the threshold voltage.

An amplifier may also be added to the output 214 of diode 206 so that the output voltage immediately saturates at the diode threshold voltage, thus resembling a step function, until a threshold value and a constant value above the threshold is attained. R₃ (i.e., resistor 208) functions generally as a bias for diode 206 (i.e., D₁) and should generally be about 10 times larger than resistor 204 (i.e., R₂). In the circuit configuration illustrated in FIG. 2, R₁ can actually be configured as a network of connections composed of many inter-connected conducting nanowires (i.e., see FIG. 3). As explained previously, such connections are analogous to the synapses of a human brain.

FIG. 3 illustrates a block diagram illustrating a network of nanoconnections 304 formed between two electrodes, in accordance with a preferred embodiment. Nanoconnections 304 (e.g., nanoconductors) depicted in FIG. 3 are generally located between input 302 and output 306. The network of nanoconnections depicted in FIG. 3 can be implemented as a network of nanoconductors. Examples of nanoconductors include devices such as, for example, nanowires, nanotubes, and nanoparticles.

Nanoconnections 304, which are analogous to the synapses of a human brain, are preferably composed of electrical conducting material (i.e., nanoconductors). It should be appreciated by those skilled in the art that such nanoconductors can be provided in a variety of shapes and sizes without departing from the teachings herein. For example, carbon particles (e.g., granules or bearings) may be used for developing nanoconnections. The nanoconductors utilized to form a connection network may be formed as a plurality of nanoparticles.

For example, carbon particles (e.g., granules or bearings) may be used for developing nanoconnections. The nanoconductors utilized to form a connection network may be formed as a plurality of nanoparticles. For example, each nanoconnection within a connection network may be formed from as a chain of carbon nanoparticles. In “Self-assembled chains of graphitized carbon nanoparticles” by Bezryadin et al., Applied Physics Letters, Vol. 74, No. 18, pp. 2699–2701, May 3, 1999, for example, a technique is reported, which permits the self-assembly of conducting nanoparticles into long continuous chains. Thus, nanoconductors which are utilized to form a physical neural network (i.e., Knowm) could be formed from such nanoparticles. It can be appreciated that the Bezryadin et al is referred to herein for general edification and illustrative purposes only and is not considered to limit the embodiments.

It can be appreciated that a connection network as disclosed herein may be composed from a variety of different types of nanoconductors. For example, a connection network may be formed from a plurality of nanoconductors, including nanowires, nanotubes and/or nanoparticles. Note that such nanowires, nanotubes and/or nanoparticles, along with other types of nanoconductors can be formed from materials such as carbon or silicon. For example, carbon nanotubes may comprise a type of nanotube that can be utilized in accordance with one or more embodiments.

As illustrated in FIG. 3, nanoconnections 304 comprise a plurality of interconnected nanoconnections, which from this point forward, can be referred to generally as a “connection network.” An individual nanoconnection may constitute a nanoconductor such as, for example, a nanowire, a nanotube, nanoparticles(s), or any other nanoconducting structures. Nanoconnections 304 may comprise a plurality of interconnected nanotubes and/or a plurality of interconnected nanowires. Similarly, nanoconnections 304 may be formed from a plurality of interconnected nanoparticles. A connection network is thus not one connection between two electrodes, but a plurality of connections between inputs and outputs. Nanotubes, nanowires, nanoparticles and/or other nanoconducting structures may be utilized, of course, to construct nanoconnections 304 between input 302 and input 306. Although a single input 302 and a single input 306 is depicted in FIG. 3, it can be appreciated that a plurality of inputs and a plurality of outputs may be implemented in accordance with the embodiments, rather than simply a single input 302 or a single output 306.

FIG. 4 illustrates a block diagram illustrating a plurality of nanoconnections 414 between inputs 404, 406, 408, 410, 412 and outputs 416 and 418 of a physical neural network, in accordance with a preferred embodiment. Inputs 404, 406, 408, 410, and 412 can provide input signals to connections 414. Output signals can then be generated from connections 414 via outputs 416 and 418. A connection network can therefore be configured from the plurality of connections 414. Such a connection network is generally associated with one or more neuron-like nodes.

The connection network also comprises a plurality of interconnected nanoconnections, wherein each nanoconnection thereof is strengthened or weakened according to an application of an electric field. A connection network is not possible if built in one layer because the presence of one connection can alter the electric field so that other connections between adjacent electrodes could not be formed. Instead, such a connection network can be built in layers, so that each connection thereof can be formed without being influenced by field disturbances resulting from other connections. This can be seen in FIG. 5.

FIG. 5 illustrates a schematic diagram of a physical neural network 500 that can be created without disturbances, in accordance with a preferred embodiment. Physical neural network 500 is composed of a first layer 558 and a second layer 560. A plurality of inputs 502, 504, 506, 508, and 510 are respectively provided to layers 558 and 560 respectively via a plurality of input lines 512, 514, 516, 518, and 520 and a plurality of input lines 522, 524, 526, 528, and 530. Input lines 512, 514, 516, 518, and 520 are further coupled to input lines 532, 534, 536, 538, and 540 such that each line 532, 534, 536, 538, and 540 is respectively coupled to nanoconnections 572, 574, 576, 578, and 580. Thus, input line 532 is connected to nanconnections 572. Input line 534 is connected to nanoconnections 574, and input line 536 is connected to nanoconnections 576. Similarly, input line 538 is connected to nanconnections 578, and input line 540 is connected to nanoconnections 580.

Nanconnections 572, 574, 576, 578, and 580 may comprise nanoconductors such as, for example, nanotubes and/or nanowires. Nanoconnections 572, 574, 576, 578, and 580 thus comprise one or more nanoconductors. Additionally, input lines 522, 524, 526, 528, and 530 are respectively coupled to a plurality of input lines 542, 544, 546, 548 and 550, which are in turn each respectively coupled to nanoconnections 582, 584, 586, 588, and 590. Thus, for example, input line 542 is connected to nanoconnections 582, while input line 544 is connected to nanoconnections 584. Similarly, input line 546 is connected to nanoconnections 586 and input line 548 is connected to nanoconnections 588. Additionally, input line 550 is connected to nanconnections 590. Box 556 and 554 generally represent simply the output and are thus illustrated connected to outputs 562 and 568. In other words, outputs 556 and 554 respectively comprise outputs 562 and 568. The aforementioned input lines and associated components thereof actually comprise physical electronic components, including conducting input and output lines and physical nanoconnections, such as nanotubes and/or nanowires.

Thus, the number of layers 558 and 560 equals the number of desired outputs 562 and 568 from physical neural network 500. In the previous two figures, every input was potentially connected to every output, but many other configurations are possible. The connection network can be made of any electrically conducting material, although the physics of it requires that they be very small so that they will align with a practical voltage. Carbon nanotubes or any conductive nanowire can be implemented in accordance with the physical neural network described herein. Such components can form connections between electrodes by the presence of an electric field. For example, the orientation and purification of carbon nanotubes has been demonstrated using ac electrophoresis in isopropyl alcohol, as indicated in “Orientation and purification of carbon nanotubes using ac electrophoresis” by Yamamoto et al., J. Phys. D: Applied Physics, 31 (1998), 34–36. Additionally, an electric-field assisted assembly technique used to position individual nanowires suspended in an electric medium between two electrodes defined lithographically on an SiO₂ substrate is indicated in “Electric-field assisted assembly and alignment of metallic nanowires,” by Smith et al., Applied Physics Letters, Vol. 77, Num. 9, Aug. 28, 2000. Such references are referred to herein for edification and illustrative purposes only.

The only general requirements for the conducting material utilized to configure the nanoconductors are that such conducting material should preferably conduct electricity, and a dipole should preferably be induced in the material when in the presence of an electric field. Alternatively, the nanoconductors utilized in association with the physical neural network described herein can be configured to include a permanent dipole that is produced by a chemical means, rather than a dipole that is induced by an electric field.

Therefore, it should be appreciated by those skilled in the art that a connection network could also be comprised of other conductive particles that may be developed or found useful in the nanotechnology arts. For example, carbon particles (or “dust”) may also be used as nanoconductors in place of nanowires or nanotubes. Such particles may include bearings or granule-like particles.

A connection network can be constructed as follows: A voltage is applied across a gap that is filled with a mixture of nanowires and a “solvent”. This mixture could be made of many things. The only requirements are that the conducting wires must be suspended in the solvent, either dissolved or in some sort of suspension, free to move around; the electrical conductance of the substance must be less than the electrical conductance of the suspended conducting wire; and the viscosity of the substance should not be too much so that the conducting wire cannot move when an electric field is applied.

The goal for such a connection network is to develop a network of connections of just the right values so as to satisfy the particular signal-processing requirement—exactly what a neural network does. Such a connection network can be constructed by applying a voltage across a space occupied by the mixture mentioned. To create the connection network, the input terminals are selectively raised to a positive voltage while the output terminals are selectively grounded. Thus, connections can gradually form between the inputs and outputs. The important requirement that makes the physical neural network functional as a neural network is that the longer this electric field is applied across a connection gap, or the greater the frequency or amplitude, the more nanotubes and/or nanowires and/or particles align and the stronger the connection thereof becomes. Thus, the connections that are utilized most frequently by the physical neural network become the strongest.

The connections can either be initially formed and have random resistances or no connections may be formed at all. By initially forming random connections, it might be possible to teach the desired relationships faster, because the base connections do not have to be built up from scratch. Depending on the rate of connection decay, having initial random connections could prove faster, although not necessarily. The connection network can adapt itself to the requirements of a given situation regardless of the initial state of the connections. Either initial condition will work, as connections that are not used will “dissolve” back into solution. The resistance of the connection can be maintained or lowered by selective activations of the connection. In other words, if the connection is not used, it will fade away, analogous to the connections between neurons in a human brain. The temperature of the solution can also be maintained at a particular value so that the rate that connections fade away can be controlled. Additionally an electric field can be applied perpendicular to the connections to weaken them, or even erase them out altogether (i.e., as in clear, zero, or reformatting of a “disk”).

The nanoconnections may or may not be arranged in an orderly array pattern. The nanoconnections (e.g., nanotubes, nanowires, etc) of a physical neural network do not have to order themselves into neatly formed arrays. They simply float in the solution, or lie at the bottom of the gap, and more or less line up in the presence an electric field. Precise patterns are thus not necessary. In fact, neat and precise patterns may not be desired. Rather, due to the non-linear nature of neural networks, precise patterns could be a drawback rather than an advantage. In fact, it may be desirable that the connections themselves function as poor conductors, so that variable connections are formed thereof, overcoming simply an “on” and “off” structure, which is commonly associated with binary and serial networks and structures thereof.

FIG. 6 illustrates a schematic diagram illustrating an example of a physical neural network 600 that can be implemented in accordance with an alternative embodiment. Note that in FIGS. 5 and 6, like parts are indicated by like reference numerals. Thus, physical neural network 600 can be configured, based on physical neural network 500 illustrated in FIG. 5. In FIG. 6, inputs 1, 2, 3, 4, and 5 are indicated, which are respectively analogous to inputs 502, 504, 506, 508, and 510 illustrated in FIG. 5. Outputs 562 and 568 are provided to a plurality of electrical components to create a first output 626 (i.e., Output 1) and a second output 628 (i.e., Output 2). Output 562 is tied to a resistor 606, which is labeled R2 and a diode 616 at node A. Output 568 is tied to a resistor 610, which is also labeled R2 and a diode 614 at node C. Resistors 606 and 610 are each tied to a ground 602.

Diode 616 is further coupled to a resistor 608, which is labeled R3, and first output 626. Additionally, resistor 608 is coupled to ground 602 and an input to an amplifier 618. An output from amplifier 618, as indicated at node B and dashed lines thereof, can be tied back to node A. A desired output 622 from amplifier 618 is coupled to amplifier 618 at node H. Diode 614 is coupled to a resistor 612 at node F. Note that resistor 612 is labeled R3. Node F is in turn coupled to an input of amplifier 620 and to second output 628 (i.e., Output 2). Diode 614 is also connected to second output 628 and an input to amplifier 620 at second output 628. Note that second output 628 is connected to the input to amplifier 620 at node F. An output from amplifier 620 is further coupled to node D, which in turn is connected to node C. A desired output 624, which is indicated by a dashed line in FIG. 6, is also coupled to an input of amplifier 620 at node E.

In FIG. 6, the training of physical neural network 600 can be accomplished utilizing, for example, op-amp devices (e.g., amplifiers 618 and 620). By comparing an output (e.g., first output 626) of physical neural network 600 with a desired output (e.g., desired output 622), the amplifier (e.g., amplifier 618) can provide feedback and selectively strengthen connections thereof. For instance, suppose it is desired to output a voltage of +V at first output 626 (i.e., Output 1) when inputs 1 and 4 are high. When inputs 1 and 4 are taken high, also assume that first output 626 is zero. Amplifier 618 can then compare the desired output (+V) with the actual output (0) and output −V. In this case, −V is equivalent to ground.

The op-amp outputs and grounds the pre-diode junction (i.e., see node A) and causes a greater electric field across inputs 1 and 4 and the layer 1 output. This increased electric field (larger voltage drop) can cause the nanoconductors in the solution between the electrode junctions to align themselves, aggregate, and form a stronger connection between the 1 and 4 electrodes. Feedback can continue to be applied until output of physical neural network 600 matches the desired output. The same procedure can be applied to every output.

In accordance with the aforementioned example, assume that Output 1 was higher than the desired output (i.e., desired output 622). If this were the case, the op-amp output can be +V and the connection between inputs 1 and 4 and layer one output can be raised to +V. Columbic repulsions between the nanoconductors can force the connection apart, thereby weakening the connection. The feedback will then continue until the desired output is obtained. This is just one training mechanism. One can see that the training mechanism does not require any computations, because it is a simple feedback mechanism.

Such a training mechanism, however, may be implemented in many different forms. Basically, the connections in a connection network must be able to change in accordance with the feedback provided. In other words, the very general notion of connections being strengthened or connections being weakened in a physical system is the essence of a physical neural network (i.e., Knowm). Thus, it can be appreciated that the training of such a physical neural network may not require a “CPU” to calculate connection values thereof. The Knowm can adapt itself. Complicated neural network solutions could be implemented very rapidly “on the fly”, much like a human brain adapts as it performs.

The physical neural network disclosed herein thus has a number of broad applications. The core concept of a Knowm, however, is basic. The very basic idea that the connection values between electrode junctions by nanoconductors can be used in a neural network devise is all that required to develop an enormous number of possible configurations and applications thereof.

Another important feature of a physical neural network is the ability to form negative connections. This is an important feature that makes possible inhibitory effects useful in data processing. The basic idea is that the presence of one input can inhibit the effect of another input. In artificial neural networks as they currently exist, this is accomplished by multiplying the input by a negative connection value. Unfortunately, with a Knowm-based device, the connection may only take on zero or positive values under such a scenario.

In other words, either there can be a connection or no connection. A connection can simulate a negative connection by dedicating a particular connection to be negative, but one connection cannot begin positive and through a learning process change to a negative connection. In general, if starts positive, it can only go to zero. In essence, it is the idea of possessing a negative connection initially that results in the simulation, because this does not occur in a human brain. Only one type of signal travels through axon/dendrites in a human brain. That signal is transferred into the flow of a neurotransmitter whose effect on the postsynaptic neuron can be either excitatory or inhibitory, depending on the neuron.

One method for solving this problem is to utilize two sets of connections for the same output, having one set represent the positive connections and the other set represent the negative connections. The output of these two layers can be compared, and the layer with the greater output will output either a high signal or a low signal, depending on the type of connection set (inhibitory or excitatory). This can be seen in FIG. 7.

FIG. 7 illustrates a schematic diagram illustrating an example of a physical neural network 700 that can be implemented in accordance with an alternative embodiment. Physical neural network 700 thus comprises a plurality of inputs 702 (not necessarily binary) which are respectively fed to layers 704, 706, 708, and 710. Each layer is analogous to the layers depicted earlier, such as for example layers 558 and 560 of FIG. 5. An output 713 of layer 704 can be connected to a resistor 712, a transistor 720 and a first input 727 of amplifier 726. Transistor 720 is generally coupled between ground 701 and first input 727 of amplifier 726. Resistor 712 is connected to a ground 701. Note that ground 701 is analogous to ground 602 illustrated in FIG. 6 and ground 210 depicted in FIG. 2. A second input 729 of amplifier 726 can be connected to a threshold voltage 756. The output of amplifier 726 can in turn be fed to an inverting amplifier 736.

The output of inverting amplifier 736 can then be input to a NOR device 740. Similarly, an output 716 of layer 706 may be connected to resistor 714, transistor 733 and a first input 733 of an amplifier 728. A threshold voltage 760 is connected to a second input 737 of amplifier 728. Resistor 714 is generally coupled between ground 701 and first input 733 of amplifier 728. Note that first input 733 of amplifier 728 is also generally connected to an output 715 of layer 706. The output of amplifier 728 can in turn be provided to NOR device 740. The output from NOR device 740 is generally connected to a first input 745 of an amplifier 744. An actual output 750 can be taken from first input 745 to amplifier 744. A desired output 748 can be taken from a second input 747 to amplifier 744. The output from amplifier 744 is generally provided at node A, which in turn is connected to the input to transistor 720 and the input to transistor 724. Note that transistor 724 is generally coupled between ground 701 and first input 733 of amplifier 728. The second input 731 of amplifier 728 can produce a threshold voltage 760.

Layer 708 provides an output 717 that can be connected to resistor 716, transistor 725 and a first input 737 to an amplifier 732. Resistor 716 is generally coupled between ground 701 and the output 717 of layer 708. The first input 737 of amplifier 732 is also electrically connected to the output 717 of layer 708. A second input 735 to amplifier 732 may be tied to a threshold voltage 758. The output from amplifier 732 can in turn be fed to an inverting amplifier 738. The output from inverting amplifier 738 may in turn be provided to a NOR device 742. Similarly, an output 718 from layer 710 can be connected to a resistor 719, a transistor 728 and a first input 739 of an amplifier 734. Note that resistor 719 is generally coupled between node 701 and the output 719 of layer 710. A second input 741 of amplifier 734 may be coupled to a threshold voltage 762. The output from of NOR device 742 is generally connected to a first input 749 of an amplifier 746. A desired output 752 can be taken from a second input 751 of amplifier 746. An actual output 754 can be taken from first input 749 of amplifier 746. The output of amplifier 746 may be provided at node B, which in turn can be tied back to the respective inputs to transistors 725 and 728. Note that transistor 725 is generally coupled between ground 701 and the first input 737 of amplifier 732. Similarly, transistor 728 is generally connected between ground 701 and the first input 739 of amplifier 734.

Note that transistors 720, 724, 725 and/or 728 each can essentially function as a switch to ground. A transistor such as, for example, transistor 720, 724, 725 and/or 728 may comprise a field-effect transistor (FET) or another type of transistor, such as, for example, a single-electron transistor (SET). Single-electron transistor (SET) circuits are essential for hybrid circuits combining quantum SET devices with conventional electronic devices. Thus, SET devices and circuits may be adapted for use with the physical neural network of the embodiments. This is particularly important because as circuit design rules begin to move into regions of the sub-100 nanometer scale, where circuit paths are only 0.001 of the thickness of a human hair, prior art device technologies will begin to fail, and current leakage in traditional transistors will become a problem. SET offers a solution at the quantum level, through the precise control of a small number of individual electrons. Transistors such as transistors 720, 724, 725 and/or 728 can also be implemented as carbon nanotube transistors.

A truth table for the output of circuit 700 is illustrated at block 780 in FIG. 7. As indicated at block 780, when an excitatory output is high and the inhibitory output is also high, the final output is low. When the excitatory output is high and the inhibitory output is low, the final output is high. Similarly, when the excitatory output is low and the inhibitory output is high, the final output is low. When the excitatory output is low and the inhibitory output is also low, the final output is low. Note that layers 704 and 708 may thus comprise excitatory connections, while layers 706 and 710 may comprise inhibitory connections.

For every desired output, two sets of connections are used. The output of a two-diode neuron can be fed into an op-amp (e.g., a comparator). If the output that the op-amp receives is low when it should be high, the op-amp outputs a low signal. This low signal can cause the transistors (e.g., transistors 720, 725) to saturate and ground out the pre-diode junction for the excitatory diode. Such a scenario can cause, as indicated previously, an increase in the voltage drop across those connections that need to increase their strength. Note that only those connections going to the excitatory diode are strengthened. Likewise, if the desired output were low when the actual output was high, the op-amp can output a high signal. This can cause the inhibitory transistor (e.g., an NPN transistor) to saturate and ground out the neuron junction of the inhibitory connections. Those connections going to the inhibitory diode can thereafter strengthen.

At all times during the learning process, a weak alternating electric field can be applied perpendicular to the connections. This can cause the connections to weaken by rotating the nanotube perpendicular to the connection direction. This perpendicular field is important because it can allow for a much higher degree of adaptation. To understand this, one must realize that the connections cannot (practically) keep getting stronger and stronger. By weakening those connections not contributing much to the desired output, we decrease the necessary strength of the needed connections and allow for more flexibility in continuous training. This perpendicular alternating voltage can be realized by the addition of two electrodes on the outer extremity of the connection set, such as plates sandwiching the connections (i.e., above and below). Other mechanisms, such as increasing the temperature of the nanotube suspension could also be used for such a purpose, although this method is perhaps a little less controllable or practical.

The circuit depicted in FIG. 7 can be separated into two separate circuits. The first part of the circuit can be composed of nanotube connections, while the second part of the circuit comprises the “neurons” and the learning mechanism (i.e., op-amps/comparator). The learning mechanism on first glance appears similar to a relatively standard circuit that could be implemented on silicon with current technology. Such a silicon implementation can thus comprise the “neuron” chip. The second part of the circuit (i.e., the connections) is thus a new type of chip, although it could be constructed with current technology. The connection chip can be composed of an orderly array of electrodes spaced anywhere from, for example, 100 nm to 1 μm or perhaps even further. In a biological system, one talks of synapses connecting neurons. It is in the synapses where the information is processed, (i.e., the “connection weights”). Similarly, such a chip can contain all of the synapses for the physical neural network. A possible arrangement thereof can be seen in FIG. 8.

FIG. 8 illustrates a schematic diagram of a chip layout 800 for a connection network that may be implemented in accordance with an alternative embodiment. FIG. 8 thus illustrates a possible chip layout for a connection chip (i.e., connection network 800) that can be implemented in accordance with one or more embodiments. Chip layout 800 includes an input array composed of plurality of inputs 801, 802, 803, 804, and 805, which are provided to a plurality of layers 806, 807, 808, 809, 810, 811, 812, 813, 814, and 815. A plurality of outputs 802 can be derived from layers 806, 807, 808, 809, 810, 811, 812, 813, 814, and 815. Inputs 801 can be coupled to layers 806 and 807, while inputs 802 can be connected to layers 808 and 809. Similarly, inputs 803 can be connected to layers 810 and 811. Also, inputs 804 can be connected to layers 812 and 813. Inputs 805 are generally connected to layers 814 and 815.

Similarly, such an input array can includes a plurality of inputs 831, 832, 833, 834 and 835 which are respectively input to a plurality of layers 816, 817, 818, 819, 820, 821, 822, 823, 824 and 825. Thus, inputs 831 can be connected to layers 816 and 817, while inputs 832 are generally coupled to layers 818 and 819. Additionally, inputs 833 can be connected to layers 820 and 821. Inputs 834 can be connected to layers 822 and 823. Finally, inputs 835 are connected to layers 824 and 825. Arrows 828 and 830 represent a continuation of the aforementioned connection network pattern. Those skilled in the art can appreciate, of course, that chip layout 800 is not intended to represent an exhaustive chip layout or to limit the scope of the invention. Many modifications and variations to chip layout 800 are possible in light of the teachings herein without departing from the scope of the embodiments. It is contemplated that the use of a chip layout, such as chip layout 800, can involve a variety of components having different characteristics.

Preliminary calculations based on a maximum etching capability of 200 nm resolution indicated that over 4 million synapses could fit on an area of approximately 1 cm². The smallest width that an electrode can possess is generally based on current lithography. Such a width may of course change as the lithographic arts advance. This value is actually about 70 nm for state-of-the-art techniques currently. These calculations are of course extremely conservative, and are not considered a limiting feature of the embodiments. Such calculations are based on an electrode with, separation, and gap of approximately 200 nm. For such a calculation, for example, 166 connection networks comprising 250 inputs and 100 outputs can fit within a one square centimeter area.

If such chips are stacked vertically, an untold number of synapses could be attained. This is two to three orders of magnitude greater than some of the most capable neural network chips out there today, chips that rely on standard methods to calculate synapse weights. Of course, the geometry of the chip could take on many different forms, and it is quite possible (based on a conservative lithography and chip layout) that many more synapses could fit in the same space. The training of a chip this size would take a fraction of the time of a comparably sized traditional chip using digital technology.

The training of such a chip is primarily based on two assumptions. First, the inherent parallelism of a physical neural network (i.e., a Knowm) can permit all training sessions to occur simultaneously, no matter now large the associated connection network. Second, recent research has indicated that near perfect aligning of nanotubes can be accomplished in approximately 15 minutes. If one considers that the input data, arranged as a vector of binary “high's” and “low's” is presented to the Knowm simultaneously, and that all training vectors are presented one after the other in rapid succession (e.g., perhaps 100 MHz or more), then each connection would “see” a different frequency in direct proportion to the amount of time that its connection is required for accurate data processing (i.e., provided by a feedback mechanism). Thus, if it only takes approximately 15 minutes to attain an almost perfect state of alignment, then this amount of time would comprise the longest amount of time required to train, assuming that all of the training vectors are presented during that particular time period.

FIG. 9 illustrates a flow chart 900 of operations illustrating operational steps that may be followed to construct a connection network, in accordance with a preferred embodiment. Initially, as indicated at block 902, a connection gap is created from a connection network structures. As indicated earlier, the goal for such a connection network is generally to develop a network of connections of “just” the right values to satisfy particular information processing requirements, which is precisely what a neural network accomplishes. As illustrated at block 904, a solution is prepared, which is composed of nanoconductors and a “solvent.” Note that the term “solvent” as utilized herein has a variable meaning, which includes the traditional meaning of a “solvent,” and also a suspension.

The solvent utilized can comprise a volatile liquid that can be confined or sealed and not exposed to air. For example, the solvent and the nanoconductors present within the resulting solution may be sandwiched between wafers of silicon or other materials. If the fluid has a melting point that is approximately at room temperature, then the viscosity of the fluid could be controlled easily. Thus, if it is desired to lock the connection values into a particular state, the associated physical neural network (i.e., Knowm) may be cooled slightly until the fluid freezes. The term “solvent” as utilized herein thus can include fluids such as for example, toluene, hexadecane, mineral oil, etc. Note that the solution in which the nanoconductors (i.e., nanoconnections) are present should generally comprise a dielectric. Thus, when the resistance between the electrodes is measured, the conductivity of the nanoconductors can be essentially measured, not that of the solvent. The nanoconductors can be suspended in the solution or can alternately lie on the bottom surface of the connection gap. The solvent may also be provided in the form of a gas.

As illustrated thereafter at block 906, the nanoconductors must be suspended in the solvent, either dissolved or in a suspension of sorts, but generally free to move around, either in the solution or on the bottom surface of the gap. As depicted next at block 908, the electrical conductance of the solution must be less than the electrical conductance of the suspended nanoconductor(s). Similarly, the electrical resistance of the solution is greater than the electrical resistance of the nanoconductor.

Next, as illustrated at block 910, the viscosity of the substance should not be too much so that the nanoconductors cannot move when an electric field (e.g., voltage) is applied. Finally, as depicted at block 912, the resulting solution of the “solvent” and the nanoconductors is thus located within the connection gap.

Note that although a logical series of steps is illustrated in FIG. 9, it can be appreciated that the particular flow of steps can be re-arranged. Thus, for example, the creation of the connection gap, as illustrated at block 902, may occur after the preparation of the solution of the solvent and nanoconductor(s), as indicated at block 904. FIG. 9 thus represents merely possible series of steps, which may be followed to create a connection network. A variety of other steps may be followed as long as the goal of achieving a connection network is achieved. Similar reasoning also applies to FIG. 10.

FIG. 10 illustrates a flow chart 1000 of operations illustrating operational steps that may be utilized to strengthen nanoconductors within a connection gap, in accordance with a preferred embodiment. As indicated at block 1002, an electric field can be applied across the connection gap discussed above with respect to FIG. 9. The connection gap can be occupied by the solution discussed above. As indicated thereafter at block 1004, to create the connection network, the input terminals can be selectively raised to a positive voltage while the output terminals are selectively grounded. As illustrated thereafter at block 1006, connections thus form between the inputs and the outputs. The important requirements that make the resulting physical neural network functional as a neural network is that the longer this electric field is applied across the connection gap, or the greater the frequency or amplitude, the more nanoconductors align and the stronger the connection becomes. Thus, the connections that get utilized the most frequently become the strongest.

As indicated at block 1008, the connections can either be initially formed and have random resistances or no connections will be formed at all. By forming initial random connections, it might be possible to teach the desired relationships faster, because the base connections do not have to be built up as much. Depending on the rate of connection decay, having initial random connections could prove to be a faster method, although not necessarily. A connection network can adapt itself to whatever is required regardless of the initial state of the connections. Thus, as indicated at block 1010, as the electric field is applied across the connection gap, the more the nonconductor(s) will align and the stronger the connection becomes. Connections (i.e., synapses) that are not used are dissolved back into the solution, as illustrated at block 1012. As illustrated at block 1014, the resistance of the connection can be maintained or lowered by selective activations of the connections. In other words, “if you do not use the connection, it will fade away,” much like the connections between neurons in a human brain.

The neurons in a human brain, although seemingly simple when viewed individually, interact in a complicated network that computes with both space and time. The most basic picture of a neuron, which is usually implemented in technology, is a summing device that adds up a signal. Actually, this statement can be made even more general by stating that a neuron adds up a signal in discrete units of time. In other words, every group of signals incident upon the neuron can be viewed as occurring in one moment in time. Summation thus occurs in a spatial manner. The only difference between one signal and another signal depends on where such signals originate. Unfortunately, this type of data processing excludes a large range of dynamic, varying situations that cannot necessarily be broken up into discrete units of time.

The example of speech recognition is a case in point. Speech occurs in the time domain. A word is understood as the temporal pronunciation of various syllables. A sentence is composed of the temporal separation of varying words. Thoughts are composed of the temporal separation of varying sentences. Thus, for an individual to understand a spoken language at all, a syllable, word, sentence or thought must exert some type of influence on another syllable, word, sentence or thought. The most natural way that one sentence can exert any influence on another sentence, in the light of neural networks, is by a form of temporal summation. That is, a neuron “remembers” the signals it received in the past.

The human brain accomplishes this feat in an almost trivial manner. When a signal reaches a neuron, the neuron has an influx of ions rush through its membrane. The influx of ions contributes to an overall increase in the electrical potential of the neuron. Activation is achieved when the potential inside the cell reaches a certain threshold. The one caveat is that it takes time for the cell to pump out the ions, something that it does at a more or less constant rate. So, if another signal arrives before the neuron has time to pump out all of the ions, the second signal will add with the remnants of the first signal and achieve a raised potential greater than that which could have occurred with only the second signal. The first signal influences the second signal, which results in temporal summation.

Implementing this in a technological manner has proved difficult in the past. Any simulation would have to include a “memory” for the neuron. In a digital representation, this requires data to be stored for every neuron, and this memory would have to be accessed continually. In a computer simulation, one must discritize the incoming data, since operations (such as summations and learning) occur serially. That is, a computer can only do one thing at a time. Transformations of a signal from the time domain into the spatial domain require that time be broken up into discrete lengths, something that is not necessarily possible with real-time analog signals in which no point exists within a time-varying signal that is uninfluenced by another point.

A physical neural network, however, is generally not digital. A physical neural network is a massively parallel analog device. The fact that actual molecules (e.g., nanoconductors) must move around (in time) makes temporal summation a natural occurrence. This temporal summation is built into the nanoconnections. The easiest way to understand this is to view the multiplicity of nanoconnections as one connection with one input into a neuron-like node (Op-amp, Comparator, etc.). This can be seen in FIG. 11.

FIG. 11 illustrates a schematic diagram of a circuit 1100 illustrating temporal summation within a neuron, in accordance with a preferred embodiment. As indicated in FIG. 11, an input 1102 can be provided to nanoconnections 1104, which in turn can provide a signal, which can be input to an amplifier 1110 (e.g., op amp) at node B. A resistor 1106 can be connected to node A, which in turn is electrically equivalent to node B. Node B can be connected to a negative input of amplifier 1100. Resistor 1108 can also be connected to a ground 1108. Amplifier 1110 provides output 1114. Note that although nanoconnections 1104 is referred to in the plural it can be appreciated that nanoconnections 1104 can comprise a single nanoconnection or a plurality of nanoconnections. For simplicity sake, however, the plural form is used to refer to nanoconnections 1104.

Input 1102 can be provided by another physical neural network (i.e., Knowm) to cause increased connection strength of nanoconnections 1104 over time. This input would most likely arrive in pulses, but could also be continuous. A constant or pulsed electric field perpendicular to the connections can serve to constantly erode the connections, so that only signals of a desired length or amplitude can cause a connection to form. Once the connection is formed, the voltage divider formed by nanoconnection 1104 and resistor 1106 can cause a voltage at node A in direct proportion to the strength of nanoconnections 1104. When the voltage at node A reaches a desired threshold, the amplifier (i.e., an op-amp and/or comparator), will output a high voltage (i.e., output 1114). The key to the temporal summation is that, just like a real neuron, it takes time for the electric field to breakdown the nanoconnections 1104, so that signals arriving close in time will contribute to the firing of the neuron (i.e., op-amp, comparator, etc.). Temporal summation has thus been achieved. The parameters of the temporal summation could be adjusted by the amplitude and frequency of the input signals and the perpendicular electric field.

FIG. 12 illustrates a block diagram illustrating a pattern recognition system 1200, which may be implemented with a physical neural network device 1222, in accordance with an alternative embodiment. Note that pattern recognition system 1200 can be implemented as a speech recognition system. Although pattern recognition system 1200 is depicted herein in the context of speech recognition, a physical neural network device (i.e., a Knowm device) may be implemented with other pattern recognition systems, such as visual and/or imaging recognition systems. FIG. 12 thus does not comprise a limiting feature of the embodiments and is presented for general edification and illustrative purposes only. Those skilled in the art can appreciate that the diagram depicted in FIG. 12 may be modified as new applications and hardware are developed. The development or use of a pattern recognition system such as pattern recognition system 1200 of FIG. 12 by no means limits the scope of the physical neural network (i.e., Knowm) disclosed herein.

FIG. 12 thus illustrates in block diagram fashion, the system structure of a speech recognition device using a neural network according to an alternative embodiment. The pattern recognition system 1200 can be provided with a CPU 1211 for performing the functions of inputting vector rows and instructor signals (vector rows) to an output layer for the learning process of a physical neural network device 1222, and changing connection weights between respective neuron devices based on the learning process. Pattern recognition system 1200 can be implemented within the context of a data-processing system, such as, for example, a personal computer or personal digital assistant (PDA), both of which are well known in the art.

The CPU 1211 can perform various processing and controlling functions, such as pattern recognition, including but not limited to speech and/or visual recognition based on the output signals from the physical neural network device 1222. The CPU 1211 is connected to a read-only memory (ROM) 1213, a random-access memory (RAM) 1214, a communication control unit 1215, a printer 1216, a display unit 1217, a keyboard 1218, an FFT (fast Fourier transform) unit 1221, a physical neural network device 1222 and a graphic reading unit 1224 through a bus line 1220 such as a data bus line. The bus line 1220 may comprise, for example, an ISA, EISA, or PCI bus.

The ROM 1213 is a read-only memory storing various programs or data used by the CPU 1211 for performing processing or controlling the learning process, and speech recognition of the physical neural network device 1222. The ROM 1213 may store programs for carrying out the learning process according to error back-propagation for the physical neural network device or code rows concerning, for example, 80 kinds of phonemes for performing speech recognition. The code rows concerning the phonemes can be utilized as second instructor signals and for recognizing phonemes from output signals of the neuron device network. Also, the ROM 1213 can store programs of a transformation system for recognizing speech from recognized phonemes and transforming the recognized speech into a writing (i.e., written form) represented by characters.

A predetermined program stored in the ROM 1213 can be downloaded and stored in the RAM 1214. RAM 1214 generally functions as a random access memory used as a working memory of the CPU 1211. In the RAM 1214, a vector row storing area can be provided for temporarily storing a power obtained at each point in time for each frequency of the speech signal analyzed by the FFT unit 1221. A value of the power for each frequency serves as a vector row input to a first input portion of the physical neural network device 1222. Further, in the case where characters or graphics are recognized in the physical neural network device, the image data read by the graphic reading unit 1224 are stored in the RAM 1214.

The communication control unit 1215 transmits and/or receives various data such as recognized speech data to and/or from another communication control unit through a communication network 1202 such as a telephone line network, an ISDN line, a LAN, or a personal computer communication network. Network 1202 may also comprise, for example, a telecommunications network, such as a wireless communications network. Communication hardware methods and systems thereof are well known in the art.

The printer 1216 can be provided with a laser printer, a bubble-type printer, a dot matrix printer, or the like, and prints contents of input data or the recognized speech. The display unit 1217 includes an image display portion such as a CRT display or a liquid crystal display, and a display control portion. The display unit 1217 can display the contents of the input data or the recognized speech as well as a direction of an operation required for speech recognition utilizing a graphical user interface (GUI).

The keyboard 1218 generally functions as an input unit for varying operating parameters or inputting setting conditions of the FFT unit 1221, or for inputting sentences. The keyboard 1218 is generally provided with a ten-key numeric pad for inputting numerical figures, character keys for inputting characters, and function keys for performing various functions. A mouse 1219 can be connected to the keyboard 1218 and serves as a pointing device.

A speech input unit 1223, such as a microphone can be connected to the FFT unit 1221. The FFT unit 1221 transforms analog speech data input from the voice input unit 1223 into digital data and carries out spectral analysis of the digital data by discrete Fourier transformation. By performing a spectral analysis using the FFT unit 1221, the vector row based on the powers of the respective frequencies are output at predetermined intervals of time. The FFT unit 1221 performs an analysis of time-series vector rows, which represent characteristics of the inputted speech. The vector rows output by the FFT 1221 are stored in the vector row storing area in the RAM 1214.

The graphic reading unit 224, provided with devices such as a CCD (Charged Coupled Device), can be used for reading images such as characters or graphics recorded on paper or the like. The image data read by the image-reading unit 1224 are stored in the RAM 1214. Note that an example of a pattern recognition apparatus, which may be modified for use with the physical neural network described herein, is disclosed in U.S. Pat. No. 6,026,358 to Tomabechi, Feb. 16, 2000, “Neural Network, A Method of Learning of a Neural Network and Phoneme Recognition Apparatus Utilizing a Neural Network.” U.S. Pat. No. 6,026,358 is incorporated herein by reference. It can be appreciated that the Tomabechi reference does not teach, suggest or anticipate the embodiments, but is discussed herein for general illustrative, background and general edification purposes only.

The implications of a physical neural network are tremendous. With existing lithography technology, many electrodes in an array such as depicted in FIG. 5 can be etched onto a wafer of silicon. The neuron-diodes, as well as the training circuitry illustrated in FIG. 6, could be built onto the same silicon wafer, although it may be desirable to have the connections on a separate chip due to the liquid solution of nanoconductors. A solution of suspended nanoconductors could be placed between the electrode connections and the chip could be packaged. The resulting “chip” would look much like a current Integrated Chip (IC) or VLSI (very large scale integrated) chips. One could also place a rather large network parallel with a computer processor as part of a larger system. Such a network, or group of networks, could add significant computational capabilities to standard computers and associated interfaces.

For example, such a chip may be constructed utilizing a standard computer processor in parallel with a large physical neural network or group of physical neural networks. A program can then be written such that the standard computer teaches the neural network to read, or create an association between words, which is precisely the same sort of task in which neural networks can be implemented. Once the physical neural network is able to read, it can be taught for example to “surf” the Internet and find material of any particular nature. A search engine can then be developed that does not search the Internet by “keywords”, but instead by meaning. This idea of an intelligent search engine has already been proposed for standard neural networks, but until now has been impractical because the network required was too big for a standard computer to simulate. The use of a physical neural network (i.e., physical neural network) as disclosed herein now makes a truly intelligent search engine possible.

A physical neural network can be utilized in other applications, such as, for example, speech recognition and synthesis, visual and image identification, management of distributed systems, self-driving cars, filtering, etc. Such applications have to some extent already been accomplished with standard neural networks, but are generally limited in expense, practicality and not very adaptable once implemented. The use of a physical neural network can permit such applications to become more powerful and adaptable. Indeed, anything that requires a bit more “intelligence” could incorporate a physical neural network. One of the primary advantages of a physical neural network is that such a device and applications thereof can be very inexpensive to manufacture, even with present technology. The lithographic techniques required for fabricating the electrodes and channels therebetween has already been perfected and implemented in industry.

Most problems in which a neural network solution is implemented are complex adaptive problems, which change in time. An example is weather prediction. The usefulness of a physical neural network is that it could handle the enormous network needed for such computations and adapt itself in real-time. An example wherein a physical neural network (i.e., Knowm) can be particularly useful is the Personal Digital Assistant (PDA). PDA's are well known in the art. A physical neural network applied to a PDA device can be advantageous because the physical neural network can ideally function with a large network that could constantly adapt itself to the individual user without devouring too much computational time from the PDA. A physical neural network could also be implemented in many industrial applications, such as developing a real-time systems control to the manufacture of various components. This systems control can be adaptable and totally tailored to the particular application, as necessarily it must.

It will be appreciated that variations of the above-disclosed and other features and functions, or alternatives thereof, may be desirably combined into many other different systems or applications. Also that various presently unforeseen or unanticipated alternatives, modifications, variations or improvements therein may be subsequently made by those skilled in the art which are also intended to be encompassed by the following claims. 

1. A pattern recognition system, comprising: a neural network based on nanotechnology, wherein said neural network comprises a plurality of nanoconnections disposed within a solution within a connection network of said plurality of nanoconnections; and a pattern input unit, which communicates with said neural network, wherein said neural network processes data input via said pattern input unit in order to recognize data patterns.
 2. The system of claim 1 further comprising a processor that communicates with said neural network.
 3. The system of claim 1 further comprising at least one memory unit for storing data processed by said neural network, wherein said at least one memory unit is associated with said pattern input unit.
 4. The system of claim 1 wherein said at least one memory unit comprises Read Only Memory (ROM).
 5. The system of claim 1 wherein said at least one memory unit comprises Random Access Memory (RAM).
 6. The system of claim 1 wherein said pattern input unit comprise a speech input unit.
 7. The system of claim 6 further comprising a Fast Fourier Transform (FFT) unit connected to said speech input unit, wherein said FFT unit communicates data to and from said neural network.
 8. The system of claim 1 further comprising a Personal Digital Assistant (PDA) in which said neural network and said pattern input unit are located.
 9. The system of claim 1 wherein said pattern input unit comprises a visual pattern input unit that interacts with said neural network to recognize visual data patterns thereof.
 10. The system of claim 1 wherein said pattern input unit comprises an imaging input that interacts with said neural network to recognize imaging data patterns.
 11. A pattern recognition system, comprising: a neural network based on nanotechnology, wherein said neural network comprises a plurality of nanoconnections disposed within a solution within a connection network of said plurality of nanoconnections; a processor that communicates with said neural network; a pattern input unit, which communicates with said neural network, wherein said neural network processes data input via said pattern input unit in order to recognize data patterns; and at least one memory unit for storing data processed by said neural network, wherein said at least one memory unit is associated with said pattern input unit.
 12. The system of claim 11 wherein said pattern input unit comprise a speech input unit.
 13. The system of claim 12 further comprising a Fast Fourier Transform (FFT) unit connected to said speech input unit, wherein said FFT unit communicates data to and from said neural network.
 14. The system of claim 11 wherein said pattern input unit comprises a visual pattern input unit that interacts with said neural network to recognize visual data patterns.
 15. The system of claim 11 wherein said pattern input unit comprises an imaging input that interacts with said neural network to recognize imaging data patterns.
 16. A pattern recognition system, comprising: a neural network based on utilizing nanotechnology, wherein said neural network comprises a plurality of nanoconnections disposed within a solution within a connection network of said plurality of nanoconnections; a processor that communicates with said neural network; a pattern input unit, which communicates with said neural network, wherein said neural network processes data input via said pattern input unit in order to recognize data patterns; at least one memory unit for storing data processed by said neural network, wherein said at least one memory unit is associated with said pattern input unit; and a Personal Digital Assistant (PDA) in which said neural network, said processor, said pattern input unit, and said at least one memory unit are embodied.
 17. The system of claim 16 wherein said pattern input unit comprise a speech input unit.
 18. The system of claim 17 further comprising a Fast Fourier Transform (FFT) unit connected to said speech input unit, wherein said FFT unit communicates data to and from said neural network.
 19. The system of claim 16 wherein said pattern input unit comprises a visual pattern input unit that interacts with said neural network to recognize visual data patterns.
 20. The system of claim 16 wherein said pattern input unit comprises an imaging input that interacts with said neural network to recognize imaging data patterns. 